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Multiple sclerosis is one of the critical autoimmune diseases that require early detection. The central nervous system (CNS) is demyelinated by multiple sclerosis (MS), which causes lesions in both the grey and white matter (GM and WM). However, the lesions in the WM are most frequently seen on routinely obtained clinical scans. Magnetic resonance imaging (MRI) is used to identify these lesions. As part of the clinical diagnostic process, the variation in lesion charge obtained in the various MRI sequences is an important criterion indicating disease progression in terms of volume and localization as well as providing information on the efficacy of therapy. Manual segmentation, while still being used, is not ideal due to its reliance on expert knowledge, time-consuming nature, and susceptibility to variations among different experts. To address these challenges, demonstrating a tremendous deal of promise to support the diagnostic procedure of professionals, deep learning is one such subject that focuses on developing sophisticated algorithms to automate diagnosis. This research compares two different deep learning techniques (PCAEClassifier, BetaVAEClassifier) for automatically analyzing brain MRI scans from multiple sclerosis (MS) patients. The goal is to identify these WMLs and classify them into two classes, the class that contains the MRIs with white matter lesions, and the other class is for MRIs that do not have these lesions, ultimately helping diagnose and assess this disease. The performance of each model was then evaluated using the metrics of accuracy, precision, recall, and F-score. It is evident that the choice of hyperparameters significantly impacts the model's performance. For the BetaVAEClassifier, the model's performance varied significantly with different beta values. With a beta (β) of 20, the model achieved an accuracy of 87.61%, maintaining a balanced performance with precision, recall, and F1-score hovering around 87%. Similarly, for the PCAEClassifier, the model's performance varied with different sparsity parameters (S). With S equal to 20, the model achieved the highest accuracy of 88.82%, accompanied by precision, recall, and F1-score of 90.38%, 86.40%, and 88.34%, respectively. These models' sensitivity to hyperparameters highlights the intricate balance required in model configuration, especially in tasks where precision and recall are crucial, such as anomaly detection. Particularly in medical diagnostics like multiple sclerosis (MS) classification, where accurately distinguishing between affected and unaffected individuals is paramount, the performance of these models holds significant promise. Leveraging their capabilities for anomaly detection in medical imaging, particularly in the context of MS classification, could lead to earlier and more accurate diagnoses, enabling timely intervention and improved patient outcomes.
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